Transportation Scenario Planning with Graph Neural Networks
arXiv:2110.13202
Abstract
Providing efficient human mobility services and infrastructure is one of the major concerns of most mid-sized to large cities around the world. A proper understanding of the dynamics of commuting flows is, therefore, a requisite to better plan urban areas. In this context, an important task is to study hypothetical scenarios in which possible future changes are evaluated. For instance, how the increase in residential units or transportation modes in a neighborhood will change the commuting flows to or from that region? In this paper, we propose to leverage GMEL, a recently introduced graph neural network model, to evaluate changes in commuting flows taking into account different land use and infrastructure scenarios. We validate the usefulness of our methodology through real-world case studies set in two large cities in Brazil.
arXiv commentPresented at the 10th International Workshop on Urban Computing, 2021
References in corpus (6)
- Inductive Representation Learning on Large Graphs
- Molecular Graph Convolutions: Moving Beyond Fingerprints
- A universal model for mobility and migration patterns
- Systematic comparison of trip distribution laws and models
- Learning Geo-Contextual Embeddings for Commuting Flow Prediction
- Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows Over Time